Bayes-based object tracking boosted by particle swarm optimization

This paper presents a novel Bayes-based object tracking framework boosted by a particle swarm optimization (PSO) algorithm, which is a population based searching algorithm. Basically two searching steps are conducted in this method. First, the object model is projected into a high-dimensional feature space, and a PSO algorithm is applied to search over this high-dimensional space and converge to some global optima, which are well-matched candidates in terms of object features. Second, a Bayes-based filter is used to identify the one with the highest possibility among these candidates under the constraint of object motion estimation. The proposed algorithm considers not only the object features but also the object motion estimation to speed up the searching procedure. Experimental results demonstrate that the proposed method is efficient and robust in object tracking.